




'''simple docstring'''







import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
lowerCAmelCase_     						=				logging.getLogger(__name__)


lowerCAmelCase_     						=				'''Hello world! cécé herlolip'''


lowerCAmelCase_     						=				namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     BertAbsConfig(
       temp_dir='.'      ,finetune_bert=UpperCAmelCase      ,large=UpperCAmelCase      ,share_emb=UpperCAmelCase      ,use_bert_emb=UpperCAmelCase      ,encoder='bert'      ,max_pos=512      ,enc_layers=6      ,enc_hidden_size=512      ,enc_heads=8      ,enc_ff_size=512      ,enc_dropout=0.2      ,dec_layers=6      ,dec_hidden_size=768      ,dec_heads=8      ,dec_ff_size=2048      ,dec_dropout=0.2      ,)
   A__       =     torch.load(UpperCAmelCase      ,lambda UpperCAmelCase      ,UpperCAmelCase						: storage						)
   A__       =     AbsSummarizer(UpperCAmelCase      ,torch.device('cpu'						)      ,UpperCAmelCase						)
   original.eval()

   A__       =     BertAbsSummarizer(UpperCAmelCase      ,torch.device('cpu'						)						)
   new_model.eval()

   # -------------------
   # Convert the weights
   # -------------------

   logging.info('convert the model'						)
   new_model.bert.load_state_dict(original.bert.state_dict()						)
   new_model.decoder.load_state_dict(original.decoder.state_dict()						)
   new_model.generator.load_state_dict(original.generator.state_dict()						)

   # ----------------------------------
   # Make sure the outpus are identical
   # ----------------------------------

   logging.info('Make sure that the models\' outputs are identical'						)
   A__       =     BertTokenizer.from_pretrained('bert-base-uncased'						)

   # prepare the model inputs
   A__       =     tokenizer.encode('This is sample éàalj\'-.'						)
   encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCAmelCase						))						)
   A__       =     torch.tensor(UpperCAmelCase						).unsqueeze(0						)
   A__       =     tokenizer.encode('This is sample 3 éàalj\'-.'						)
   decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCAmelCase						))						)
   A__       =     torch.tensor(UpperCAmelCase						).unsqueeze(0						)

   # failsafe to make sure the weights reset does not affect the
   # loaded weights.
   assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight						)						) == 0

   # forward pass
   A__       =     encoder_input_ids
   A__       =     decoder_input_ids
   A__       =     A__       =     None
   A__       =     None
   A__       =     A__       =     None
   A__       =     A__       =     None
   A__       =     None

   # The original model does not apply the geneator layer immediatly but rather in
   # the beam search (where it combines softmax + linear layer). Since we already
   # apply the softmax in our generation process we only apply the linear layer here.
   # We make sure that the outputs of the full stack are identical
   A__       =     original(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)[0]
   A__       =     original.generator(UpperCAmelCase						)

   A__       =     new_model(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)[0]
   A__       =     new_model.generator(UpperCAmelCase						)

   A__       =     torch.max(torch.abs(output_converted_model - output_original_model						)						).item()
   print('Maximum absolute difference beween weights: {:.2f}'.format(UpperCAmelCase						)						)
   A__       =     torch.max(torch.abs(output_converted_generator - output_original_generator						)						).item()
   print('Maximum absolute difference beween weights: {:.2f}'.format(UpperCAmelCase						)						)

   A__       =     torch.allclose(UpperCAmelCase      ,UpperCAmelCase      ,atol=1e-3						)
   if are_identical:
      logging.info('all weights are equal up to 1e-3'						)
   else:
      raise ValueError('the weights are different. The new model is likely different from the original one.'						)

   # The model has been saved with torch.save(model) and this is bound to the exact
   # directory structure. We save the state_dict instead.
   logging.info('saving the model\'s state dictionary'						)
   torch.save(
       new_model.state_dict()      ,'./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    parser.add_argument(
        '''--bertabs_checkpoint_path''',
        default=None,
        type=str,
        required=True,
        help='''Path the official PyTorch dump.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default=None,
        type=str,
        required=True,
        help='''Path to the output PyTorch model.''',
    )
    lowerCAmelCase_     						=				parser.parse_args()

    convert_bertabs_checkpoints(
        args.bertabs_checkpoint_path,
        args.pytorch_dump_folder_path,
    )








'''simple docstring'''







import socket







def 						_A      ( ):






   '''simple docstring'''







   A__       =     socket.socket(socket.AF_INET      ,socket.SOCK_STREAM						)
   A__       =     socket.gethostname()
   A__       =     12312

   sock.connect((host, port)						)
   sock.send(B'Hello server!'						)

   with open('Received_file'      ,'wb'						) as out_file:
      print('File opened'						)
      print('Receiving data...'						)
      while True:
         A__       =     sock.recv(1024						)
         if not data:
            break
         out_file.write(UpperCAmelCase						)

   print('Successfully received the file'						)
   sock.close()
   print('Connection closed'						)


if __name__ == "__main__":
    main()


